The increasing availability of digital data on human activities has led to the emergence of computational social science, a research field at the interface of computer science, mathematical modeling and social sciences. Among the concepts that have attracted much attention, we find "success". The premise of a science of success rests on observing that a difference exists between performance and success: Performance, representing the totality of objectively measurable achievements in a certain domain of activity, like the publication record of a scientist or the winning record of an athlete or a team, captures the actions of an individual entity. In contrast, success, captured by fame, celebrity, popularity, impact or visibility, is a collective measure, representing a community’s reaction to and acceptance of an individual entity’s performance. The link between these two measures, while is often taken for granted, is actually far from being understood and often controversial.
Roberta Sinatra and Renaud Lambiotte
Advances in Complex Systems Vol. 21, No. 03n04, 1802001 (2018) https://doi.org/10.1142/S0219525918020010
The hot streak—loosely defined as ‘winning begets more winnings’—highlights a specific period during which an individual’s performance is substantially better than his or her typical performance. Although hot streaks have been widely debated in sports, gambling and financial markets over the past several decades, little is known about whether they apply to individual careers. Here, building on rich literature on the lifecycle of creativity, we collected large-scale career histories of individual artists, film directors and scientists, tracing the artworks, films and scientific publications they produced. We find that, across all three domains, hit works within a career show a high degree of temporal regularity, with each career being characterized by bursts of high-impact works occurring in sequence. We demonstrate that these observations can be explained by a simple hot-streak model, allowing us to probe quantitatively the hot streak phenomenon governing individual careers. We find this phenomemon to be remarkably universal across diverse domains: hot streaks are ubiquitous yet usually unique across different careers. The hot streak emerges randomly within an individual’s sequence of works, is temporally localized, and is not associated with any detectable change in productivity. We show that, because works produced during hot streaks garner substantially more impact, the uncovered hot streaks fundamentally drive the collective impact of an individual, and ignoring this leads us to systematically overestimate or underestimate the future impact of a career. These results not only deepen our quantitative understanding of patterns that govern individual ingenuity and success, but also may have implications for identifying and nurturing individuals whose work will have lasting impact.
Hot streaks in artistic, cultural, and scientific careers
Lu Liu, Yang Wang, Roberta Sinatra, C. Lee Giles, Chaoming Song & Dashun Wang
Nature volume 559, pages 396–399 (2018)
The tropics contain the overwhelming majority of Earth’s biodiversity: their terrestrial, freshwater and marine ecosystems hold more than three-quarters of all species, including almost all shallow-water corals and over 90% of terrestrial birds. However, tropical ecosystems are also subject to pervasive and interacting stressors, such as deforestation, overfishing and climate change, and they are set within a socio-economic context that includes growing pressure from an increasingly globalized world, larger and more affluent tropical populations, and weak governance and response capacities. Concerted local, national and international actions are urgently required to prevent a collapse of tropical biodiversity.
The future of hyperdiverse tropical ecosystems
Jos Barlow, et al.
Nature volume 559, pages 517–526 (2018)
Turing’s theory of pattern formation is a universal model for self-organization, applicable to many systems in physics, chemistry, and biology. Essential properties of a Turing system, such as the conditions for the existence of patterns and the mechanisms of pattern selection, are well understood in small networks. However, a general set of rules explaining how network topology determines fundamental system properties and constraints has not been found. Here we provide a first general theory of Turing network topology, which proves why three key features of a Turing system are directly determined by the topology: the type of restrictions that apply to the diffusion rates, the robustness of the system, and the phase relations of the molecular species.
Key Features of Turing Systems are Determined Purely by Network Topology
Xavier Diego, Luciano Marcon, Patrick Müller, and James Sharpe
Phys. Rev. X 8, 021071